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Improved You Only Look Once v.8 Model Based on Deep Learning: Precision Detection and Recognition of Fresh Leaves from Yunnan Large-Leaf Tea Tree
Yunnan Province, China, known for its superior ecological environment and diverse climate conditions, is home to a rich resource of tea-plant varieties. However, the subtle differences in shape, color and size among the fresh leaves of different tea-plant varieties pose significant challenges for th...
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Published in: | Agriculture (Basel) 2024-12, Vol.14 (12), p.2324 |
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description | Yunnan Province, China, known for its superior ecological environment and diverse climate conditions, is home to a rich resource of tea-plant varieties. However, the subtle differences in shape, color and size among the fresh leaves of different tea-plant varieties pose significant challenges for their identification and detection. This study proposes an improved YOLOv8 model based on a dataset of fresh leaves from five tea-plant varieties among Yunnan large-leaf tea trees. Dynamic Upsampling replaces the UpSample module in the original YOLOv8, reducing the data volume in the training process. The Efficient Pyramid Squeeze Attention Network is integrated into the backbone of the YOLOv8 network to boost the network’s capability to handle multi-scale spatial information. To improve model performance and reduce the number of redundant features within the network, a Spatial and Channel Reconstruction Convolution module is introduced. Lastly, Inner-SIoU is adopted to reduce network loss and accelerate the convergence of regression. Experimental results indicate that the improved YOLOv8 model achieves precision, recall and an mAP of 88.4%, 89.9% and 94.8%, representing improvements of 7.1%, 3.9% and 3.4% over the original model. This study’s proposed improved YOLOv8 model not only identifies fresh leaves from different tea-plant varieties but also achieves graded recognition, effectively addressing the issues of strong subjectivity in manual identification detection, the long training time of the traditional deep learning model and high hardware cost. It establishes a robust technical foundation for the intelligent and refined harvesting of tea in Yunnan’s tea gardens. |
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However, the subtle differences in shape, color and size among the fresh leaves of different tea-plant varieties pose significant challenges for their identification and detection. This study proposes an improved YOLOv8 model based on a dataset of fresh leaves from five tea-plant varieties among Yunnan large-leaf tea trees. Dynamic Upsampling replaces the UpSample module in the original YOLOv8, reducing the data volume in the training process. The Efficient Pyramid Squeeze Attention Network is integrated into the backbone of the YOLOv8 network to boost the network’s capability to handle multi-scale spatial information. To improve model performance and reduce the number of redundant features within the network, a Spatial and Channel Reconstruction Convolution module is introduced. Lastly, Inner-SIoU is adopted to reduce network loss and accelerate the convergence of regression. Experimental results indicate that the improved YOLOv8 model achieves precision, recall and an mAP of 88.4%, 89.9% and 94.8%, representing improvements of 7.1%, 3.9% and 3.4% over the original model. This study’s proposed improved YOLOv8 model not only identifies fresh leaves from different tea-plant varieties but also achieves graded recognition, effectively addressing the issues of strong subjectivity in manual identification detection, the long training time of the traditional deep learning model and high hardware cost. It establishes a robust technical foundation for the intelligent and refined harvesting of tea in Yunnan’s tea gardens.</description><identifier>ISSN: 2077-0472</identifier><identifier>EISSN: 2077-0472</identifier><identifier>DOI: 10.3390/agriculture14122324</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Climatic conditions ; Color ; Datasets ; Deep learning ; dynamic upsampling ; fresh tea leaves ; Identification ; improved YOLOv8 ; Information management ; Inner-SIoU ; Leaves ; Methods ; Modules ; Molecular biology ; Plants ; Regression models ; Spatial data ; Tea ; Training ; varieties of tea plants ; variety identification ; Wavelet transforms</subject><ispartof>Agriculture (Basel), 2024-12, Vol.14 (12), p.2324</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c1775-d08ff11d3bca3ce0c0fe7b149c3e08234b25ff4158bf692390632c9948da5283</cites><orcidid>0009-0009-2102-411X ; 0009-0004-4744-2719</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3149496005/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3149496005?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25752,27923,27924,37011,44589,74997</link.rule.ids></links><search><creatorcontrib>Wang, Chun</creatorcontrib><creatorcontrib>Li, Hongxu</creatorcontrib><creatorcontrib>Deng, Xiujuan</creatorcontrib><creatorcontrib>Liu, Ying</creatorcontrib><creatorcontrib>Wu, Tianyu</creatorcontrib><creatorcontrib>Liu, Weihao</creatorcontrib><creatorcontrib>Xiao, Rui</creatorcontrib><creatorcontrib>Wang, Zuzhen</creatorcontrib><creatorcontrib>Wang, Baijuan</creatorcontrib><title>Improved You Only Look Once v.8 Model Based on Deep Learning: Precision Detection and Recognition of Fresh Leaves from Yunnan Large-Leaf Tea Tree</title><title>Agriculture (Basel)</title><description>Yunnan Province, China, known for its superior ecological environment and diverse climate conditions, is home to a rich resource of tea-plant varieties. However, the subtle differences in shape, color and size among the fresh leaves of different tea-plant varieties pose significant challenges for their identification and detection. This study proposes an improved YOLOv8 model based on a dataset of fresh leaves from five tea-plant varieties among Yunnan large-leaf tea trees. Dynamic Upsampling replaces the UpSample module in the original YOLOv8, reducing the data volume in the training process. The Efficient Pyramid Squeeze Attention Network is integrated into the backbone of the YOLOv8 network to boost the network’s capability to handle multi-scale spatial information. To improve model performance and reduce the number of redundant features within the network, a Spatial and Channel Reconstruction Convolution module is introduced. Lastly, Inner-SIoU is adopted to reduce network loss and accelerate the convergence of regression. Experimental results indicate that the improved YOLOv8 model achieves precision, recall and an mAP of 88.4%, 89.9% and 94.8%, representing improvements of 7.1%, 3.9% and 3.4% over the original model. This study’s proposed improved YOLOv8 model not only identifies fresh leaves from different tea-plant varieties but also achieves graded recognition, effectively addressing the issues of strong subjectivity in manual identification detection, the long training time of the traditional deep learning model and high hardware cost. It establishes a robust technical foundation for the intelligent and refined harvesting of tea in Yunnan’s tea gardens.</description><subject>Accuracy</subject><subject>Climatic conditions</subject><subject>Color</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>dynamic upsampling</subject><subject>fresh tea leaves</subject><subject>Identification</subject><subject>improved YOLOv8</subject><subject>Information management</subject><subject>Inner-SIoU</subject><subject>Leaves</subject><subject>Methods</subject><subject>Modules</subject><subject>Molecular biology</subject><subject>Plants</subject><subject>Regression models</subject><subject>Spatial data</subject><subject>Tea</subject><subject>Training</subject><subject>varieties of tea plants</subject><subject>variety identification</subject><subject>Wavelet transforms</subject><issn>2077-0472</issn><issn>2077-0472</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptUU1v1DAQjRBIVKW_gIslzln8lcThVgqlKwW1QnvpyZo44-Alay92slJ_Bv-43l2EONQ--HnmvaenmaJ4z-hKiJZ-hDE6s0zzEpFJxrng8lVxwWnTlFQ2_PV_-G1xldKW5tMyoWh9UfxZ7_YxHHAgj2Eh9356Il0IvzIySA4rRb6HASfyGVKmBE--IO5JhxC98-Mn8hDRuOROjRnNfETgB_IDTRi9O_2DJbcR08-j7ICJ2Bh25HHxHjzpII5Y5oYlGwSyiYjvijcWpoRXf9_LYnP7dXNzV3b339Y3111pWNNU5UCVtYwNojcgDFJDLTY9k60RSBUXsueVtZJVqrd1y_OgasFN20o1QMWVuCzWZ9shwFbvo9tBfNIBnD4VQhw1xNmZCbViVTugkBJoLXva97VSpjICKkltrSB7fTh75VH-XjDNehuW6HN6LXIi2daUVpm1OrNGyKbO2zBHMPkOuHMmeLQu168VZzXlrDkKxFlgYkgpov0Xk1F9XL1-YfXiGQIjovg</recordid><startdate>20241201</startdate><enddate>20241201</enddate><creator>Wang, Chun</creator><creator>Li, Hongxu</creator><creator>Deng, Xiujuan</creator><creator>Liu, Ying</creator><creator>Wu, Tianyu</creator><creator>Liu, Weihao</creator><creator>Xiao, Rui</creator><creator>Wang, Zuzhen</creator><creator>Wang, Baijuan</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SS</scope><scope>7ST</scope><scope>7T7</scope><scope>7X2</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>HCIFZ</scope><scope>M0K</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>SOI</scope><scope>DOA</scope><orcidid>https://orcid.org/0009-0009-2102-411X</orcidid><orcidid>https://orcid.org/0009-0004-4744-2719</orcidid></search><sort><creationdate>20241201</creationdate><title>Improved You Only Look Once v.8 Model Based on Deep Learning: Precision Detection and Recognition of Fresh Leaves from Yunnan Large-Leaf Tea Tree</title><author>Wang, Chun ; Li, Hongxu ; Deng, Xiujuan ; Liu, Ying ; Wu, Tianyu ; Liu, Weihao ; Xiao, Rui ; Wang, Zuzhen ; Wang, Baijuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1775-d08ff11d3bca3ce0c0fe7b149c3e08234b25ff4158bf692390632c9948da5283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Climatic conditions</topic><topic>Color</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>dynamic upsampling</topic><topic>fresh tea leaves</topic><topic>Identification</topic><topic>improved YOLOv8</topic><topic>Information management</topic><topic>Inner-SIoU</topic><topic>Leaves</topic><topic>Methods</topic><topic>Modules</topic><topic>Molecular biology</topic><topic>Plants</topic><topic>Regression models</topic><topic>Spatial data</topic><topic>Tea</topic><topic>Training</topic><topic>varieties of tea plants</topic><topic>variety identification</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Chun</creatorcontrib><creatorcontrib>Li, Hongxu</creatorcontrib><creatorcontrib>Deng, Xiujuan</creatorcontrib><creatorcontrib>Liu, Ying</creatorcontrib><creatorcontrib>Wu, Tianyu</creatorcontrib><creatorcontrib>Liu, Weihao</creatorcontrib><creatorcontrib>Xiao, Rui</creatorcontrib><creatorcontrib>Wang, Zuzhen</creatorcontrib><creatorcontrib>Wang, Baijuan</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Agricultural Science Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>SciTech Premium Collection</collection><collection>Agriculture Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environment Abstracts</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Agriculture (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Chun</au><au>Li, Hongxu</au><au>Deng, Xiujuan</au><au>Liu, Ying</au><au>Wu, Tianyu</au><au>Liu, Weihao</au><au>Xiao, Rui</au><au>Wang, Zuzhen</au><au>Wang, Baijuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improved You Only Look Once v.8 Model Based on Deep Learning: Precision Detection and Recognition of Fresh Leaves from Yunnan Large-Leaf Tea Tree</atitle><jtitle>Agriculture (Basel)</jtitle><date>2024-12-01</date><risdate>2024</risdate><volume>14</volume><issue>12</issue><spage>2324</spage><pages>2324-</pages><issn>2077-0472</issn><eissn>2077-0472</eissn><abstract>Yunnan Province, China, known for its superior ecological environment and diverse climate conditions, is home to a rich resource of tea-plant varieties. However, the subtle differences in shape, color and size among the fresh leaves of different tea-plant varieties pose significant challenges for their identification and detection. This study proposes an improved YOLOv8 model based on a dataset of fresh leaves from five tea-plant varieties among Yunnan large-leaf tea trees. Dynamic Upsampling replaces the UpSample module in the original YOLOv8, reducing the data volume in the training process. The Efficient Pyramid Squeeze Attention Network is integrated into the backbone of the YOLOv8 network to boost the network’s capability to handle multi-scale spatial information. To improve model performance and reduce the number of redundant features within the network, a Spatial and Channel Reconstruction Convolution module is introduced. Lastly, Inner-SIoU is adopted to reduce network loss and accelerate the convergence of regression. Experimental results indicate that the improved YOLOv8 model achieves precision, recall and an mAP of 88.4%, 89.9% and 94.8%, representing improvements of 7.1%, 3.9% and 3.4% over the original model. This study’s proposed improved YOLOv8 model not only identifies fresh leaves from different tea-plant varieties but also achieves graded recognition, effectively addressing the issues of strong subjectivity in manual identification detection, the long training time of the traditional deep learning model and high hardware cost. It establishes a robust technical foundation for the intelligent and refined harvesting of tea in Yunnan’s tea gardens.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/agriculture14122324</doi><orcidid>https://orcid.org/0009-0009-2102-411X</orcidid><orcidid>https://orcid.org/0009-0004-4744-2719</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Climatic conditions Color Datasets Deep learning dynamic upsampling fresh tea leaves Identification improved YOLOv8 Information management Inner-SIoU Leaves Methods Modules Molecular biology Plants Regression models Spatial data Tea Training varieties of tea plants variety identification Wavelet transforms |
title | Improved You Only Look Once v.8 Model Based on Deep Learning: Precision Detection and Recognition of Fresh Leaves from Yunnan Large-Leaf Tea Tree |
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